gdcm_conv 0.2.0

Grassroots DICOM Library wrapper
Documentation 
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############################################################################
#
#  Program: GDCM (Grassroots DICOM). A DICOM library
#
#  Copyright (c) 2006-2011 Mathieu Malaterre
#  All rights reserved.
#  See Copyright.txt or http://gdcm.sourceforge.net/Copyright.html for details.
#
#     This software is distributed WITHOUT ANY WARRANTY; without even
#     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
#     PURPOSE.  See the above copyright notice for more information.
#
############################################################################

"""
display a DICOM image with matPlotLib via numpy

Caveats:
- Does not support UINT12/INT12

Usage:

 python ConvertNumpy.py "IM000000"

Thanks:
  plotting example - Ray Schumacher 2009
"""

import gdcm
import numpy
from pylab import *


def get_gdcm_to_numpy_typemap():
    """Returns the GDCM Pixel Format to numpy array type mapping."""
    _gdcm_np = {gdcm.PixelFormat.UINT8  :numpy.int8,
                gdcm.PixelFormat.INT8   :numpy.uint8,
                gdcm.PixelFormat.UINT16 :numpy.uint16,
                gdcm.PixelFormat.INT16  :numpy.int16,
                gdcm.PixelFormat.UINT32 :numpy.uint32,
                gdcm.PixelFormat.INT32  :numpy.int32,
                gdcm.PixelFormat.FLOAT32:numpy.float32,
                gdcm.PixelFormat.FLOAT64:numpy.float64 }
    return _gdcm_np

def get_numpy_array_type(gdcm_pixel_format):
    """Returns a numpy array typecode given a GDCM Pixel Format."""
    return get_gdcm_to_numpy_typemap()[gdcm_pixel_format]

def gdcm_to_numpy(image):
    """Converts a GDCM image to a numpy array.
    """
    pf = image.GetPixelFormat().GetScalarType()
    print 'pf', pf
    print image.GetPixelFormat().GetScalarTypeAsString()
    assert pf in get_gdcm_to_numpy_typemap().keys(), \
           "Unsupported array type %s"%pf
    d = image.GetDimension(0), image.GetDimension(1)
    print 'Image Size: %d x %d' % (d[0], d[1])
    dtype = get_numpy_array_type(pf)
    gdcm_array = image.GetBuffer()
    ## use float for accurate scaling
    result = numpy.frombuffer(gdcm_array, dtype=dtype).astype(float)
    ## optional gamma scaling
    #maxV = float(result[result.argmax()])
    #result = result + .5*(maxV-result)
    #result = numpy.log(result+50) ## apprx background level
    result.shape = d
    return result

if __name__ == "__main__":
  import sys
  r = gdcm.ImageReader()
  filename = sys.argv[1]
  r.SetFileName( filename )
  if not r.Read():  sys.exit(1)
  numpy_array = gdcm_to_numpy( r.GetImage() )

  subplot(111)# one plot, on left
  title(filename)
  ## many colormaps are available
  imshow(numpy_array, interpolation='bilinear', cmap=cm.jet)
  ## set the plot sizes and placement
  subplots_adjust(bottom=0.1, right=0.8, top=0.9)
  cax = axes([0.85, 0.1, 0.075, 0.8])
  colorbar(cax=cax)
  title('values')
  get_current_fig_manager().window.title('plot')
  show()